1. Scope of the Invention
The present invention relates to image processing and more specifically to segmenting 3D structures from a series of cardiac images.
2. Related Prior Art
Medical images of a subject's heart typically are comprised of several slices throughout the 3-D volume of the heart. In addition, the heart may be imaged at several different instants (phases) in the cardiac cycle. Thus the ensemble of all images gives a picture of the heart during the complete course of one heart beat.
It is often desirable to use this set of images to extract quantitative information about the heart motion useful in medical diagnosis. Such information includes measurement of the cardiac blood volume, the ejection fraction and the amount of wall motion. It is necessary to detect and differentiate different contiguous anatomical regions of the heart to perform this measurement. Identification of contiguous regions of the same material is known as segmentation. It is particularly important to be able to segment, and measure blood volume in a subject's heart chambers for diagnosis of heart disease.
Segmentation of the left ventricle is particularly important because of its important physiological function.
Other methods have been used for extracting the left ventricle in a medical image set. One method is to model the volume as a polyhedron, and vary modeling parameters to induce the model to conform with the measured blood mass, as set forth in "Constrained Deformable Superquadrics And Non-Rigid Motion Tracking", by D. Metaxas and D. Terzopoulos, IEEE Computer Vision and Pattern Recognition, pp. 337-343, 1991.
3D templates may also be used instead of a polyhedron. Methods using 3D templates tend to be very slow, and at present, are unsuitable for clinical use.
An alternative method is to model the ventricle wall by a curve in each image and to carry out segmentation one image at a time. These techniques are generally known as "snake" techniques. Their disadvantage is that they are generally quite dependent upon initial unknown parameters. Also, convergence speed is an issue.
Simple thresholding has also been used for extracting regions of images. A method of selecting a threshold for separating an image into two regions according to intensity has been described by Nobuyuki Otsu in "Discriminant and Least-Squares Threshold Selection" Proceedings of the Fourth international Joint Conference on Pattern Recognition, Kyoto Japan, 1978, pp. 592-596. This algorithm will be referred to as DTSM thresholding. Thresholding using a single threshold across all slices and phases is usually unsuccessful because of image intensity changes from image to image. These intensity changes are caused by different blood flow velocities, and changing imaging parameters.
Currently, there is a need for a more accurate system which segments structures within 3D volume images acquired at different time periods of a cycle.